Program, method, information processing device, and system

Abstract

The user's pre-disease state can be detected from the results of health checkups, which can be easily obtained. [Solution] The program is a program to be executed by a computer having a processor and a memory, and causes the processor to execute the following steps: acquiring measurement values ​​of a user's first test item and second test item; inputting the acquired second test item of the user into a trained model that outputs a predicted value of the first test item when the measurement value of the second test item is input, and outputting a predicted value of the user's first test item; determining the user's pre-disease state based on the measurement values ​​and predicted value of the user's first test item; and presenting the determined pre-disease state of the user.

Description

[Technical Field]

[0001] The present disclosure relates to a program, a method, an information processing device, and a system. [Background technology]

[0002] Techniques for estimating the risk of a specific disease are known. For example, Patent Document 1 discloses a technique for obtaining the tendency of future changes in eGFR by inputting time series data of past eGFR measurements obtained in a health checkup and time series data of measurements other than eGFR obtained in the same health checkup into a trained machine learning model. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2025-52775 Summary of the Invention [Problem to be solved by the invention]

[0004] However, Patent Document 1 only predicts the future trend of changes in eGFR, and is unable to detect a user's pre-disease state using the predicted value and the measured value.

[0005] The purpose of the present disclosure is to detect a user's pre-disease state from the results of a health checkup or the like, which can be easily obtained. [Means for solving the problem]

[0006] In order to solve the above-mentioned problems, one embodiment of a program of the present disclosure is a program to be executed by a computer having a processor and a memory, the program causing the processor to execute the following steps: acquiring measurement values ​​of a first test item and a second test item of a user; inputting the acquired second test item of the user into a trained model that outputs a predicted value of the first test item when the measurement value of the second test item is input, and outputting a predicted value of the first test item of the user; determining the pre-disease state of the user based on the measurement values ​​and predicted value of the first test item of the user; and presenting the determined pre-disease state of the user. [Effects of the Invention]

[0007] According to the present disclosure, a user's pre-disease state can be detected from the results of a health checkup or the like, which can be easily obtained. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a block diagram showing an example of the overall configuration of a system 1. FIG. [Figure 2] 2 is a diagram illustrating an example of a functional configuration of a user terminal 10 shown in FIG. [Figure 3] 2 is a diagram illustrating an example of a functional configuration of a server 20 shown in FIG. [Figure 4] FIG. 2 is a diagram showing the data structure of a user information table 2021. [Figure 5] FIG. 2 is a diagram showing the data structure of a health index information table 2022. [Figure 6] 10 is a flowchart showing an example of the learning processing operation of the trained model 2023. [Figure 7] 10 is a flowchart illustrating an example of an operation for measuring a user's pre-disease state. [Figure 8] 10 is a flowchart illustrating an example of a process for creating a health index distribution chart. [Figure 9] 10 is a flowchart illustrating an example of a process for determining a pre-disease state. [Figure 10]1 is a schematic diagram illustrating an example of a display screen of a display 141 of a user terminal 10. FIG. [Figure 11] FIG. 2 is a block diagram showing the basic hardware configuration of a computer 90. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. In all drawings describing the embodiments, common components are designated by the same reference numerals, and repeated description will be omitted. Note that the following embodiments do not unduly limit the content of the present disclosure described in the claims. Furthermore, not all components shown in the embodiments are necessarily essential components of the present disclosure. Furthermore, each drawing is a schematic diagram and is not necessarily a precise illustration.

[0010] In the following description, a "processor" refers to one or more processors. The at least one processor is typically a microprocessor such as a CPU (Central Processing Unit), but may also be another type of processor such as a GPU (Graphics Processing Unit). The at least one processor may be single-core or multi-core.

[0011] Furthermore, the at least one processor may be a processor in the broad sense, such as a hardware circuit (for example, a field-programmable gate array (FPGA) or an application-specific integrated circuit (ASIC)) that performs part or all of the processing.

[0012] In the following explanation, information that produces an output in response to an input may be described using the expression "xxx database," but this information may be data of any structure, or may be a learning model such as a neural network that produces an output in response to an input. Therefore, "xxx database" may be referred to as "xxx information."

[0013] Furthermore, in the following description, the configuration of each database is an example, and one database may be divided into two or more databases, or all or part of two or more databases may be one database.

[0014] In addition, in the following explanation, processing may be described using the "program" as the subject, but since a program is executed by a processor to perform specified processing while appropriately using a memory unit and / or an interface unit, etc., the subject of the processing may also be the processor (or a device such as a controller that has that processor).

[0015] The program may be installed in a device such as a computer, or may be stored in, for example, a program distribution server or a computer-readable (e.g., non-transitory) recording medium. Also, in the following description, two or more programs may be realized as one program, or one program may be realized as two or more programs.

[0016] Furthermore, in the following description, identification numbers are used as identification information for various objects, but other types of identification information (for example, identifiers including alphabetic characters or symbols) may also be used.

[0017] In the following description, the control lines and information lines are those that are considered necessary for the description, and do not necessarily represent all control lines and information lines in the product. All components may be interconnected.

[0018] Each information processing device is configured by a computer equipped with an arithmetic unit and a storage device. The basic hardware configuration of the computer and the basic functional configuration of the computer realized by the hardware configuration will be described later. For the server 20 and the user terminal 10, descriptions that overlap with the basic hardware configuration and basic functional configuration of the computer will be omitted.

[0019] <Summary> The system according to this embodiment has the function of calculating a health index from predetermined test items of a user obtained from a medical checkup or the like, and determining the user's pre-disease state based on the health index. The system acquires measurements of the user's first test item and second test item, inputs the acquired second test item of the user into a trained model, and outputs a predicted value of the user's first test item. The system determines the user's pre-disease state based on the measurements and predicted value of the user's first test item, and presents the determined pre-disease state of the user.

[0020] The first test item may include, for example, at least one of BMI (Body Mass Index) and LH ratio (LDL-C / HDL-C ratio) in a health checkup. The second test item may include, for example, all test items in a health checkup other than BMI and LH ratio. The second test item may include, for example, test items that are not functionally related to the first test item. For example, if BMI is the first test item, the second test item may exclude weight, height, and waist circumference. For example, if the LH ratio is the first test item, the second test item may exclude HDL cholesterol level (HDL-CHOLE), LDL cholesterol level (LDL-CHOLE), and TOT cholesterol level (TOT-CHOLE).

[0021] The system according to this embodiment can detect a user's pre-disease state from easily obtainable results of health checkups, etc. This allows the user to become aware of a pre-disease state where resilience is declining at an individual level and return to a healthy state while sufficient resilience is still available.

[0022] <1 Overall system configuration> Fig. 1 is a block diagram showing an example of the overall configuration of a system 1. The system 1 shown in Fig. 1 includes, for example, a user terminal 10 and a server 20. The user terminal 10 and the server 20 are connected for communication via, for example, a network 80.

[0023] 1 shows an example in which the system 1 includes one user terminal 10, but the number of user terminals 10 included in the system 1 is not limited to one. The number of user terminals 10 included in the system 1 may be two or more.

[0024] In this embodiment, a collection of multiple devices may be considered as one server. The allocation of multiple functions required to realize the server 20 according to this embodiment to one or more pieces of hardware can be determined appropriately in consideration of the processing capacity of each piece of hardware and / or the specifications required for the server 20.

[0025] The user terminal 10 shown in Fig. 1 is, for example, an information processing device operated by a user who measures the pre-disease state. The user terminal 10 accepts various information and instructions from the user and transmits the input information to the server 20. The user terminal 10 displays the user's pre-disease state and improvement measures, etc., presented by the server 20. The user terminal 10 is realized, for example, by a desktop personal computer (PC), a laptop PC, etc. The user terminal 10 may also be realized by a mobile terminal such as a smartphone or a tablet.

[0026] The user terminal 10 includes a communication IF (Interface) 12, an input device 13, an output device 14, a memory 15, a storage 16, and a processor 19. The input device 13 is a device for receiving input operations from a user (for example, a touch panel, a touch pad, a pointing device such as a mouse, a keyboard, etc.). The output device 14 is a device for presenting information to a user (a display, a speaker, etc.).

[0027] The server 20 is, for example, an information processing device that detects a user's pre-disease state and provides a service (for example, a health management application) that presents the results. The server 20, for example, acquires measured values ​​of a first test item and a second test item of the user, calculates a predicted value of the first test item using a trained model, and determines the pre-disease state based on the measured values ​​and the predicted values.

[0028] The server 20 is, for example, an information processing device realized by a computer connected to a network 80. As shown in Fig. 1, the server 20 includes a communication IF 22, an input / output IF 23, a memory 25, a storage 26, and a processor 29. The input / output IF 23 functions as an input device for receiving input operations from a user and as an interface with an output device for outputting information to the user.

[0029] Each information processing device is configured by a computer equipped with an arithmetic unit and a storage device. The basic hardware configuration of the computer and the basic functional configuration of the computer realized by the hardware configuration will be described later. For the user terminal 10 and the server 20, descriptions that overlap with the basic hardware configuration and basic functional configuration of the computer will be omitted.

[0030] <1-1 Functional configuration of user terminal> Fig. 2 is a diagram showing an example of the functional configuration of the user terminal 10 shown in Fig. 1. As shown in Fig. 2, the user terminal 10 includes a communication unit 120, an input device 13, an output device 14, an audio processing unit 17, a microphone 171, a speaker 172, a storage unit 180, and a control unit 190. The blocks included in the user terminal 10 are electrically connected by, for example, a bus or the like.

[0031] The communication unit 120 performs processing such as modulation and demodulation for the user terminal 10 to communicate with other devices. The communication unit 120 performs transmission processing on signals generated by the control unit 190 and transmits the signals to the outside (for example, the server 20). The communication unit 120 performs reception processing on signals received from the outside and outputs the signals to the control unit 190.

[0032] The input device 13 is a device for inputting instructions or information by a user operating the user terminal 10. The input device 13 is realized, for example, by a touch-sensitive device 131 or the like, which inputs instructions by touching the operation surface. If the user terminal 10 is a PC or the like, the input device 13 may be realized by a reader, keyboard, mouse, or the like. The input device 13 converts instructions input by the user into electrical signals and outputs the electrical signals to the control unit 190. The input device 13 may also include, for example, a receiving port that receives electrical signals input from an external input device.

[0033] The output device 14 is a device for presenting information to a user operating the user terminal 10. The output device 14 is realized, for example, by a display 141 or the like. The display 141 displays data according to the control of the control unit 190. The display 141 is realized, for example, by an LCD (Liquid Crystal Display) or an organic EL (Electro-Luminescence) display or the like.

[0034] The audio processing unit 17 performs, for example, digital-to-analog conversion processing of an audio signal. The audio processing unit 17 converts a signal provided from the microphone 171 into a digital signal and provides the converted signal to the control unit 190. The audio processing unit 17 also provides the audio signal to the speaker 172. The audio processing unit 17 is realized, for example, by a processor for audio processing. The microphone 171 receives audio input and provides an audio signal corresponding to the audio input to the audio processing unit 17. The speaker 172 converts the audio signal provided from the audio processing unit 17 into audio and outputs the audio to the outside of the user terminal 10.

[0035] The storage unit 180 is realized by, for example, the memory 15, the storage 16, etc., and stores data and programs used by the user terminal 10. The storage unit 180 stores, for example, user information 181.

[0036] The user information 181 includes, for example, a user ID, a user name, user information, health check information, and the like.

[0037] The control unit 190 is realized by the processor 19 reading a program stored in the storage unit 180 and executing instructions included in the program. The control unit 190 controls the operation of the user terminal 10. The control unit 190 functions as an operation reception unit 191, a transmission / reception unit 192, and a presentation control unit 193 by operating in accordance with the program.

[0038] The operation reception unit 191 performs processing for receiving instructions or information input from the input device 13. Specifically, for example, the operation reception unit 191 receives instructions or information input from the touch-sensitive device 131 or the like.

[0039] Furthermore, the operation reception unit 191 receives voice instructions input from the microphone 171. Specifically, for example, the operation reception unit 191 receives a voice signal that is input from the microphone 171 and converted into a digital signal by the voice processing unit 17. For example, the operation reception unit 191 analyzes the received voice signal and extracts a predetermined noun, thereby acquiring an instruction from the user.

[0040] The transmitting / receiving unit 192 performs processing for the user terminal 10 to transmit and receive data to and from external devices such as the server 20 in accordance with a communication protocol. Specifically, for example, the transmitting / receiving unit 192 transmits information input by the user or instructions from the user to the server 20. The transmitting / receiving unit 192 also receives information provided by the server 20.

[0041] The presentation control unit 193 controls the output device 14 to present information provided from the server 20 to the user. Specifically, for example, the presentation control unit 193 causes the display 141 to display request instructions transmitted from the server 20, acceptance of various requests, and the like. In addition, the presentation control unit 193 causes the speaker 172 to output the information transmitted from the server 20.

[0042] <1-2 Functional configuration of the server> Fig. 3 is a diagram showing an example of the functional configuration of the server 20 shown in Fig. 1. As shown in Fig. 3, the server 20 functions as a communication unit 201, a storage unit 202, and a control unit 203.

[0043] The communication unit 201 performs processing for the server 20 to communicate with external devices.

[0044] The storage unit 202 includes, for example, a user information table 2021, a health index information table 2022, and a trained model 2023. The databases and trained models stored in the storage unit 202 are not limited to these.

[0045] The user information table 2021 is a database that stores information about users, including their health checkup information. Details will be described later. The user information table 2021 may, for example, be linked to the user information 181 in the storage unit 180 of the user terminal 10. Furthermore, the user information table 2021 may acquire the user's health checkup information by, for example, linking with a system in a health checkup institution or medical institution.

[0046] The health index information table 2022 is a database that stores distribution IDs, other user classifications, health indexes, distribution maps, etc. Details will be described later.

[0047] The trained model 2023 is a model that outputs a predicted value of the first test item when a measurement value of the second test item of a user is input. The trained model 2023 is trained using the past measurement values ​​of the second test item of multiple other users as training data. The trained model 2023 may also use the past measurement values ​​of the first test item of multiple other users as training data. It is desirable that the multiple other users who are the subject of the training data have normal ranges for all test items, but this is not limited to this.

[0048] The control unit 203 is realized by the processor 29 reading a program stored in the storage unit 202 and executing instructions included in the program. The program includes an application such as a web browser application. The program includes an interpreted programming language such as JavaScript (registered trademark) that is executed on a web browser application stored in the user terminal 10. The control unit 203 operates in accordance with the program to perform functions shown as a reception control module 2031, a transmission control module 2032, a service processing module 2033, and a presentation control module 2034.

[0049] The reception control module 2031 controls the process in which the server 20 receives signals from external devices in accordance with a communication protocol. The transmission control module 2032 controls the process in which the server 20 transmits signals to external devices in accordance with a communication protocol. The service processing module 2033 acquires measurement values ​​of the user's first test item and second test item, calculates a predicted value of the first test item using the trained model 2023, and determines a pre-disease state based on the measurement values ​​and predicted values. The presentation control module 2034 controls the process in which information is presented to the user.

[0050] Specifically, the service processing module 2033 acquires measurement values ​​of the user's first test item and second test item. The first test item includes, for example, at least one of BMI and LH ratio in a health checkup. The second test item includes, for example, all test items in a health checkup other than the first test item. The second test item includes, for example, a test item that is not functionally related to the first test item. The service processing module 2033 inputs the acquired second test item of the user into the trained model 2023. The trained model 2023 is a model that outputs a predicted value of the first test item when the measurement value of the second test item is input. The service processing module 2033 acquires the predicted value of the user's first test item output from the trained model 2023. The service processing module 2033 determines the user's pre-disease state based on the measurement value and predicted value of the user's first test item. The service processing module 2033 presents the determined pre-disease state of the user. The service processing module 2033 may propose improvement measures along with the determination result.

[0051] <2 Data Structure> 4 and 5 are diagrams showing the data structure of the databases stored in the server 20. Note that Figures 4 and 5 are merely examples and do not exclude data that is not listed. Furthermore, even data that is listed in the same database may be stored in separate storage areas in the storage unit 202.

[0052] Fig. 4 is a diagram showing the data structure of the user information table 2021. The user information table 2021 shown in Fig. 4 is a database having columns such as name, age, sex, date of birth, medical checkup information, and health index, with the user ID as a key. The user information table 2021 stores, for example, information about a user whose pre-disease state is being measured.

[0053] The user ID is an item for storing an identifier for uniquely identifying a user.

[0054] The name is an item for storing the user's name.

[0055] Age is an item for storing the age of the user.

[0056] Gender is an item for storing the gender of the user.

[0057] The date of birth is an item for storing the user's date of birth.

[0058] The medical checkup information is an item that stores information about the user's medical checkup. The item "medical checkup information" stores, for example, measurement values ​​of various test items (first test item and second test item) in a medical checkup. The item "medical checkup information" is not limited to, for example, measurement values, and may also store a predicted value of the first test item. These measurement values ​​and predicted values ​​may be stored, for example, as numerical data.

[0059] The health index is an item that stores the user's health index. The item "health index" stores a health index consisting of the error between the measured value and predicted value of the user's first test item. The health index is an index consisting of the difference (for example, actual measured value - predicted value) between the predicted value of the first test item calculated by the trained model and the actually measured value of the first test item. This health index is an index that indicates the current state of imbalance in health (pre-illness state) taking into account the user's constitution and variations in individual health conditions.

[0060] Fig. 5 is a diagram showing the data structure of health index information table 2022. Health index information table 2022 shown in Fig. 5 is a database having columns such as other user classification, health index, and distribution map, with distribution ID as a key. Health index information table 2022 stores health index distribution maps created based on the health indices of multiple other users.

[0061] The distribution ID is an item for storing an identifier for uniquely identifying a health index distribution map.

[0062] The other user classification is an item that stores the characteristics of the group of other users (for example, a group in a healthy state) that the health index distribution map relates to. The item "other user classification" stores group information classified by a specific health state or constitution, such as a "group in the normal range (healthy state) (pre-disease level 1)," a "group with test items in the caution range (pre-disease level 2)," or a "group with test items in the abnormal range (pre-disease level 3)." Pre-disease level 1 indicates, for example, a healthy state, pre-disease level 2 indicates, for example, a pre-disease state (a state close to a healthy state before the symptoms of pre-disease appear), and pre-disease level 3 indicates, for example, a pre-disease state (a state in which the user is not yet sick, but is in a declining health state and is susceptible to illness).

[0063] The health index is an item that stores the past health index of another user. The item "health index" stores a health index consisting of the error between the measured value and predicted value of the first test item of another user. The health index stores the difference (e.g., actual measured value - predicted value) between the predicted value of the first test item calculated by the trained model and the actually measured value of the first test item.

[0064] The distribution map is an item that stores a health index distribution map. The item "distribution map" stores, for example, a distribution map of health indexes consisting of errors between measured values ​​and predicted values ​​of multiple individual items (e.g., BMI and LH ratio) included in the first test items of multiple other users. The distribution map is, for example, a graph with the health index of each of the multiple individual items as an axis.

[0065] <3 Example of operation> <3-1 Learning process of trained model> A description will be given of the learning process operation of the trained model 2023. The trained model 2023 is a model that outputs a predicted value of a first test item when a measured value of a second test item of a user is input.

[0066] FIG. 6 is a flowchart showing an example of the learning process operation of the trained model 2023.

[0067] In step S11, the server 20 acquires past health checkup information of other users. Specifically, the service processing module 2033 acquires, for example, past measurement values ​​of the first and second test items of multiple other users. This information is acquired via the network 80 from, for example, a system of a health checkup institution or medical institution, or a health management application entered by an individual, and is stored in the health index information table 2022.

[0068] The first test items include multiple individual items, such as BMI and LH ratio. The first test items may also include pulse pressure as an individual item. The first test items are used as dependent variables, but may also be used as explanatory variables. For example, if the dependent variable is BMI, the LH ratio may be used as the explanatory variable, and if the dependent variable is LH ratio, the BMI may be used as the explanatory variable. The second test items are explanatory variables used to predict the first test items, and may include a wide range of health checkup items, such as weight, height, age, sex, blood pressure (systolic blood pressure, diastolic blood pressure), blood glucose level, cholesterol levels (TOT cholesterol level, HDL cholesterol level, LDL cholesterol level), liver function test results, kidney function test results, uric acid level, white blood cell count, red blood cell count, hemoglobin level, anemia-related items, platelet count, and body composition information (body fat percentage, muscle mass). However, the second test items are items that do not have a functional relationship with the first test items. For example, if the first test item is BMI, the second test items exclude weight, height, and waist circumference. Also, if the first test item is LH ratio, the second test items exclude HDL cholesterol level, LDL cholesterol level, and TOT cholesterol level.

[0069] In step S12, the server 20 trains the trained model 2023. Specifically, the service processing module 2033 trains the trained model 2023 using the measurement values ​​of the second test items of multiple other users acquired in step S11 as training data. At this time, the service processing module 2033 may also use the measurement values ​​of the first test item as training data. For example, the trained model 2023 may be trained based on the second test item serving as an explanatory variable and the first test item serving as a target variable as training data. It is desirable that the training data be data of other users whose test items are within the normal range, but this is not limited to this.

[0070] In step S13, the server 20 stores the trained trained model 2023 in the storage unit 202.

[0071] <3-2 Measuring the user's pre-illness state> The operation of measuring the user's pre-disease state will be described.

[0072] FIG. 7 is a flowchart showing an example of the operation for measuring the user's pre-illness state.

[0073] In step S21, the server 20 creates a health index distribution map. Details of this process will be described later with reference to the flowchart in FIG.

[0074] Next, the user who wishes to measure their pre-disease state accesses server 20, which operates a health management application. The user logs in to the health management application provided by server 20, for example, by inputting their own user ID and password. Server 20 receives a request from the user to measure their pre-disease state.

[0075] In step S22, the server 20 acquires health checkup information of the user who is the subject of pre-disease state measurement. Specifically, the service processing module 2033 acquires the measurement values ​​of the user's first test item and second test item, for example, via the user terminal 10 operated by the user. The service processing module 2033 may acquire the measurement values ​​of the user's first test item and second test item from, for example, a system of a health checkup institution or medical institution. This information is stored in the user information table 2021.

[0076] The first test items include a plurality of individual items, such as BMI and LH ratio. The first test items may include pulse pressure as an individual item. The second test items may include a wide range of health checkup items, such as weight, height, age, sex, blood pressure (systolic blood pressure, diastolic blood pressure), blood glucose level, cholesterol level (TOT cholesterol level, HDL cholesterol level, LDL cholesterol level), liver function test results, kidney function test results, uric acid level, white blood cell count, red blood cell count, hemoglobin level, items related to anemia, platelet count, and body composition information (body fat percentage, muscle mass).

[0077] In step S23, the server 20 calculates a predicted value for the user's first test item. Specifically, the service processing module 2033 inputs the measurement value of the user's second test item acquired in step S22 into the trained model 2023. The trained model 2023 is a model that has been trained to output a predicted value for the first test item when the measurement value of the second test item is input. The trained model 2023 outputs a predicted value for the first test item based on the input second test item of the user. At this time, the service processing module 2033 causes the trained model 2023 to output a predicted value for each of the multiple individual items (BMI and LH ratio) included in the first test item.

[0078] For example, the service processing module 2033 inputs the measured value of an item that is not functionally related to BMI as the second test item into the trained model 2023, and outputs a predicted value of BMI as the first test item. In this case, the second test item may include the LH ratio. Also, for example, the service processing module 2033 inputs the measured value of an item that is not functionally related to LH ratio as the second test item into the trained model 2023, and outputs a predicted value of the LH ratio as the first test item. In this case, the second test item may include BMI.

[0079] In step S24, the server 20 determines the user's pre-disease state based on the acquired measurement value of the first test item and the calculated predicted value of the first test item. Details of this process will be described later with reference to the flowchart in FIG. 9.

[0080] In step S25, the server 20 presents the determination result. Specifically, the service processing module 2033 causes the display 141 of the user terminal 10 to display the determined pre-disease state of the user via the presentation control module 2034. The service processing module 2033 may, for example, cause the display 141 to superimpose the user's health index on a health index distribution chart via the presentation control module 2034. The service processing module 2033 may also suggest improvement measures (for example, recommending a specific behavior taking into consideration the user's constitution) along with the determination result via the presentation control module 2034.

[0081] <3-3 Creation process of health index distribution map> 8 is a flowchart showing an example of the process of creating a health index distribution chart, which shows the detailed process of step S21 in FIG.

[0082] In step S211, the server 20 acquires health checkup information of multiple other users. Specifically, the service processing module 2033 acquires, for example, measurement values ​​of the first test item and the second test item of the multiple other users. The other users are different from the user whose pre-disease state is being measured, and are users who have previously undergone health checkups and whose result information has been accumulated in each database. This information is acquired via the network 80 from, for example, a system of a health checkup institution or a medical institution, or a health management application entered by an individual, and is stored in the health index information table 2022. Note that the process of step S211 in FIG. 8 may be the same as the process of step S11 in FIG. 6, and does not need to be performed again.

[0083] In step S212, the server 20 calculates predicted values ​​of the first test items of multiple other users. Specifically, the service processing module 2033 inputs the measurement values ​​of the second test items of each of the multiple other users acquired in step S211 into the trained model 2023. The trained model 2023 is a model trained to output a predicted value of the first test item when the measurement values ​​of the second test item are input. The trained model 2023 outputs a predicted value of the first test item based on the input second test item of the other users. At this time, the service processing module 2033 causes the trained model 2023 to output a predicted value for each of multiple individual items (BMI and LH ratio) included in the first test item.

[0084] For example, the service processing module 2033 inputs the measured value of an item that is not functionally related to BMI as the second test item into the trained model 2023, and outputs a predicted value of BMI as the first test item. In this case, the second test item may include the LH ratio. Also, for example, the service processing module 2033 inputs the measured value of an item that is not functionally related to LH ratio as the second test item into the trained model 2023, and outputs a predicted value of the LH ratio as the first test item. In this case, the second test item may include BMI.

[0085] In step S213, server 20 calculates health indices for multiple other users. Specifically, service processing module 2033 calculates health indices consisting of errors between measured values ​​and predicted values ​​of a first test item for multiple other users. Service processing module 2033 calculates, for example, the difference between the measured value and predicted value of the first test item (e.g., measured value - predicted value) as the error between the measured value and predicted value of the first test item. At this time, service processing module 2033 calculates health indices for each of multiple individual items (BMI and LH ratio) included in the first test item.

[0086] In step S214, server 20 creates a health index distribution map. Specifically, service processing module 2033 creates the health index distribution map based on the health indexes of the multiple other users calculated in step S213. This health index distribution map shows the distribution of health indexes consisting of errors between measured values ​​and predicted values ​​of multiple first test items for multiple other users. The health index distribution map is a graph with the health index of each of multiple individual items (BMI and LH ratio) as the axis, and for example, the horizontal axis is the BMI error (measured value - predicted value) and the vertical axis is the LH ratio error (measured value - predicted value). This graph is displayed on a logarithmic scale, for example. The health index distribution map includes multiple distribution maps (distribution groups). The multiple distribution groups are divided according to the classification of the other users, for example, into a "normal range (healthy state) group (pre-disease level 1)," a "group with test items in the caution range (pre-disease level 2)," and a "group with test items in the abnormal range (pre-disease level 3)." The multiple distribution maps may be created as separate graphs at the same scale, or may be created on a single graph. Each of the multiple distribution groups is shown as a contour density plot. The health index distribution maps are stored in a health index information table 2022.

[0087] <3-4 Pre-disease state determination processing operation>

[0088] 9 is a flowchart showing an example of the pre-disease state determination process, which shows the detailed process of step S24 in FIG.

[0089] In step S241, the server 20 calculates the user's health index. Specifically, the service processing module 2033 calculates the health index consisting of the error between the measured value and the predicted value of the user's first test item. The service processing module 2033 calculates, for example, the difference between the measured value and the predicted value of the first test item (for example, actual measurement value - predicted value) as the error between the measured value and the predicted value of the first test item. At this time, the service processing module 2033 calculates the health index for each of the multiple individual items (BMI and LH ratio) included in the first test item.

[0090] In step S242, server 20 compares the user's health index with the health index distribution map. Specifically, service processing module 2033 corresponds (superimposes) the user's health index calculated in step S241 on the health index distribution map created in step S21, and compares the position of the user's health index with the position of the distribution group on the health index distribution map.

[0091] In step S243, server 20 determines the user's pre-illness state. Specifically, service processing module 2033 determines the user's pre-illness state based on the comparison result between the user's health index and the health index distribution map. That is, service processing module 2033 checks the comparison result in which the user's health index is superimposed on the health index distribution map, and identifies the position (distribution group) in the health index distribution map in which the user's health index exists. For example, if the user's health index is in the "group with test items in the caution range (pre-illness level 2)," service processing module 2033 determines the user's pre-illness state to be pre-illness level 2.

[0092] <4 Screen example> 10 is a schematic diagram showing an example of a display screen of the display 141 of the user terminal 10. For example, FIG. 10 shows a determination result presentation screen in step S25 shown in FIG.

[0093] The screen shown in FIG. 10 includes a first area 1411, a second area 1412, and a third area 1413.

[0094] The first area 1411 is an area where, for example, a health index distribution chart and the user's current health index are displayed. Here, the first area 1411 includes three distribution charts divided by the classification of other users. Each of the three distribution charts is a graph with, for example, the horizontal axis representing the BMI error and the vertical axis representing the LH ratio error. This graph is displayed, for example, on a logarithmic scale. Each of the three distribution charts is divided by the classification of other users, for example, into a "normal range (healthy state) group (pre-disease level 1)," a "group with test items in the caution range (pre-disease level 2)," and a "group with test items in the abnormal range (pre-disease level 3)." Each of the three distribution charts may be color-coded for each group. Each of the three distribution charts is displayed as a contour density plot. Note that in this example, the three distribution charts are displayed as three separate graphs, but they may also be displayed superimposed as a single graph. The user's current health index is displayed as an arrow superimposed on one of the three distribution charts. Here, the user's current health index belongs to the "group with test items in the caution range (pre-disease level 2)."

[0095] The second area 1412 is an area that illustrates, for example, the user's current health condition (pre-illness state). Here, the second area 1412 displays the balance of the user's health condition in correspondence with the balance of the image of a scale. This allows the user to visually and immediately understand the balance of their own health condition.

[0096] The third area 1413 is an area where, for example, the assessment result and a message suggesting improvement measures are displayed. Here, the third area 1413 displays the assessment result of pre-disease level 2 and a message of improvement measures, such as a message like "Your health condition seems to be losing balance around the arrow, so try to restore balance now. Specifically, take your constitution into consideration and focus on XX."

[0097] <5 Variations> In the above embodiment, the trained model 2023 outputs the predicted value of the first test item based on the test items of a health checkup, etc. However, this embodiment is not limited to this. In this embodiment, the predicted value of the first test item may be calculated by, for example, regression analysis.

[0098] Furthermore, in the above embodiment, the pre-disease state is determined using BMI and LH ratio as the first test items, but this embodiment is not limited to this. In this embodiment, the pre-disease state may be determined using either BMI or LH ratio. Specifically, the server 20 creates a health index distribution map consisting of errors between measured and predicted BMI values ​​of multiple other users, for example. This health index distribution map is a graph in which the horizontal axis represents the measured BMI value and the vertical axis represents the BMI error (measured value - predicted value). The server 20 calculates a health index consisting of errors between the measured and predicted BMI values ​​of the user, for example, and determines the pre-disease state by correlating it with the health index distribution map.

[0099] In the above embodiment, the server 20 displays the health index distribution map as a two-dimensional logarithmic scale contour density plot, but this embodiment is not limited to this. In this embodiment, the server 20 may display the health index distribution map in multiple dimensions using health indices other than the errors (measured value-predicted value) of the BMI and LH ratio, or health indices such as test items other than the BMI and LH ratio.

[0100] Furthermore, in this embodiment, the server 20 determines the pre-disease state based on the user's health index at a single point in time (for example, only the present), but this embodiment is not limited to this. In this embodiment, the server 20 may determine the pre-disease state based on time-series changes in the user's health index (changes from the past to the present). Specifically, when the server 20 confirms that the user's health index has moved (or is about to move) from the distribution of pre-disease level 1 to the distribution of pre-disease level 2 on the health index distribution chart due to time-series changes, the server 20 may determine that the balance of the user's health state is about to be disrupted.

[0101] In addition, in the above embodiment, an example of a form in which each function is provided in the user terminal 10 and the server 20 is described, but this is not limited to this form, and some or all of the functions may be provided in the user terminal 10 and the server 20 in a form different from the above embodiment.

[0102] <6 Summary> As described above, the server 20 of this embodiment acquires the measured values ​​of the first test item and the second test item of the user. The server 20 inputs the acquired second test item of the user into a trained model that outputs a predicted value of the first test item when the measured value of the second test item is input, and outputs a predicted value of the first test item of the user. The server 20 determines the user's pre-disease state based on the measured value and predicted value of the user's first test item and presents the determined pre-disease state of the user. This makes it possible to detect the user's pre-disease state from the results of a health check, etc., which can be easily obtained. As a result, a pre-disease state in which resilience is declining at the individual level can be noticed, and the user can return to a healthy state while sufficient resilience is still retained.

[0103] In addition, in the determination step, server 20 of this embodiment calculates a health index consisting of the error between the measured value and the predicted value of the user's first test item, and determines the user's pre-disease state based on the user's health index. This allows the health index consisting of the error between the measured value and the predicted value to be quantitatively evaluated, which can be used to determine the pre-disease state.

[0104] Furthermore, the server 20 of this embodiment creates a health index distribution map based on health indexes consisting of errors between the measured values ​​and predicted values ​​of the first test items of multiple other users. In the determination step, the server 20 determines the user's pre-disease state by comparing the user's health index with the health index distribution map. This makes it possible to more objectively and appropriately determine the pre-disease state, taking into account the diversity of individual constitutions and health states.

[0105] In this embodiment, the first test item includes multiple individual items, and the health index distribution chart is a graph with the axis representing a health index consisting of the error between the measured value and the predicted value for each of the multiple individual items for multiple other users. This allows visual understanding of the relationship between the measured value and the predicted value for multiple different individual items, and enables multifaceted analysis of the pre-disease state.

[0106] In this embodiment, the graph is a logarithmic scale contour density plot, which allows the distribution of data to be displayed in greater detail and over a wider range, making it easier to grasp the distribution trend.

[0107] In this embodiment, the first test item includes at least one of BMI and LH ratio, and the second test item includes an item that is not functionally related to BMI and LH ratio. This allows for the determination of a pre-disease state to be performed using items that can be easily obtained in a general health checkup.

[0108] In this embodiment, if the first test item is BMI, the second test items do not include weight, height, and waist size. By excluding the direct factors that make up BMI, a predicted BMI value can be calculated from more indirect factors, enabling pre-disease detection that reflects differences in physical constitution.

[0109] In this embodiment, when the first test item is the LH ratio, the second test items exclude HDL cholesterol, LDL cholesterol, and TOT cholesterol. By excluding the direct factors that make up the LH ratio, the predicted value of the LH ratio can be calculated from more indirect factors, enabling pre-disease detection that reflects differences in constitution.

[0110] <7 Basic computer hardware configuration> 11 is a block diagram showing the basic hardware configuration of a computer 90. The computer 90 includes at least a processor 91, a main memory device 92, an auxiliary memory device 93, and a communication IF (interface) 99. These are electrically connected to each other by a bus.

[0111] The processor 91 is hardware for executing an instruction set written in a program, and is composed of an arithmetic unit, registers, peripheral circuits, and the like.

[0112] The main storage device 92 is used to temporarily store programs, data to be processed by the programs, etc. For example, it is a volatile memory such as a DRAM (Dynamic Random Access Memory).

[0113] The auxiliary storage device 93 is a storage device for saving data and programs, such as a flash memory, a hard disk drive (HDD), a magneto-optical disk, a CD-ROM, a DVD-ROM, or a semiconductor memory.

[0114] The communication IF 99 is an interface for inputting and outputting signals for communicating with other computers via a network using wired or wireless communication standards.

[0115] The network is composed of the Internet, a LAN, various mobile communication systems constructed by wireless base stations, etc. For example, the network includes 3G, 4G, and 5G mobile communication systems, LTE (Long Term Evolution), and wireless networks (e.g., Wi-Fi (registered trademark)) that can connect to the Internet via a predetermined access point. In the case of a wireless connection, communication protocols include, for example, Z-Wave (registered trademark), ZigBee (registered trademark), and Bluetooth (registered trademark). In the case of a wired connection, the network also includes a direct connection using a USB (Universal Serial Bus) cable, etc.

[0116] It should be noted that the computer 90 can be virtually realized by distributing all or part of each hardware configuration across multiple computers 90 and interconnecting them via a network. In this way, the computer 90 is a concept that includes not only a computer 90 housed in a single housing or case, but also a virtualized computer system.

[0117] <8 Basic Functional Configuration of Computer 90> A description will be given of the functional configuration of a computer realized by the basic hardware configuration of a computer 90 shown in Fig. 11. The computer includes at least the functional units of a control unit, a storage unit, and a communication unit.

[0118] The functional units of the computer 90 can also be realized by distributing all or part of the functional units among multiple computers 90 interconnected via a network. The computer 90 is a concept that includes not only a single computer 90 but also a virtualized computer system.

[0119] The control unit is realized by the processor 91 reading various programs stored in the auxiliary storage device 93, expanding them in the main storage device 92, and executing processing in accordance with the programs. The control unit can realize functional units that perform various types of information processing depending on the type of program. In this way, the computer is realized as an information processing device that processes information.

[0120] The storage unit is realized by a main storage device 92 and an auxiliary storage device 93. The storage unit stores data, various programs, and various databases. Furthermore, the processor 91 can allocate a storage area corresponding to the storage unit in the main storage device 92 or the auxiliary storage device 93 in accordance with the programs. Furthermore, the control unit can cause the processor 91 to execute processes for adding, updating, and deleting data stored in the storage unit in accordance with the various programs.

[0121] A database refers to a relational database, which is used to manage and correlate data sets called tabular databases, which are structurally defined by rows and columns. In a database, a table is called a database, a column in a table, and a row in a table is called a record. In a relational database, relationships between databases can be set and associated.

[0122] Typically, each database has a column set as a key for uniquely identifying a record, but setting a key to a column is not essential. The control unit can cause the processor 91 to add, delete, or update records in a specific database stored in the storage unit according to various programs.

[0123] The communication unit is realized by the communication IF 99. The communication unit realizes the function of communicating with other computers 90 via a network. The communication unit can receive information transmitted from other computers 90 and input the information to the control unit. The control unit can cause the processor 91 to execute information processing on the received information in accordance with various programs. In addition, the communication unit can transmit information output from the control unit to other computers 90.

[0124] Furthermore, the above-described configurations, functions, processing units, processing means, etc. may be partially or entirely implemented in hardware, for example, by designing them as integrated circuits. The present invention can also be realized by software program code that implements the functions of the embodiments. In this case, a storage medium on which the program code is recorded is provided to a computer, and a processor included in the computer reads the program code stored in the storage medium. In this case, the program code itself read from the storage medium implements the functions of the above-described embodiments, and the program code itself and the storage medium on which it is stored constitute the present invention. Examples of storage media for providing such program code include flexible disks, CD-ROMs, DVD-ROMs, hard disks, SSDs, optical disks, magneto-optical disks, CD-Rs, magnetic tape, non-volatile memory cards, and ROMs.

[0125] Furthermore, the program code that realizes the functions described in this embodiment can be implemented in a wide range of program or script languages, such as assembler, C / C++, perl, Shell, PHP, and Java (registered trademark).

[0126] Furthermore, the program code of the software that realizes the functions of the embodiments may be distributed via a network and stored in a storage means such as a computer's hard disk or memory, or in a storage medium such as a CD-RW or CD-R, and the processor of the computer may read and execute the program code stored in the storage means or storage medium.

[0127] The functions performed by the components described herein may be implemented in circuitry or processing circuitry, including general-purpose processors, application-specific processors, integrated circuits, ASICs (Application Specific Integrated Circuits), a CPU (a Central Processing Unit), conventional circuits, and / or combinations thereof, programmed to perform the described functions. A processor includes transistors and other circuits and is considered to be circuitry or processing circuitry. A processor may also be a programmed processor that executes programs stored in memory.

[0128] In this specification, a circuitry, unit, or means is hardware that is programmed to realize or performs the described functions, which may be any hardware disclosed herein or any hardware known to be programmed to realize or perform the described functions.

[0129] If the hardware is a processor considered to be a type of circuitry, the circuitry, means, or unit is a combination of the hardware and software used to configure the hardware and / or processor.

[0130] Although several embodiments of the present disclosure have been described above, these embodiments can be embodied in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and modifications are intended to be included in the scope of the inventions and their equivalents as defined in the claims, as well as in the scope and spirit of the inventions.

[0131] (Addendum) The matters described in the above embodiments will be supplemented below.

[0132] (Appendix 1) A program to be executed by a computer having a processor and a memory, the program causing the processor to: acquiring measurements of a first test item and a second test item of a user; a step of inputting the acquired second inspection item of the user into a trained model that outputs a predicted value of the first inspection item when a measured value of the second inspection item is input, and outputting the predicted value of the first inspection item of the user; A step of determining a pre-disease state of the user based on the measured value and predicted value of the first test item of the user; presenting the determined non-disease state of the user; A program that executes the following.

[0133] (Appendix 2) In the determining step, a health index consisting of an error between a measured value and a predicted value of the first test item of the user is calculated, and the pre-disease state of the user is determined based on the health index of the user. (Appendix 1) describes the program.

[0134] (Appendix 3) creating a health index distribution map based on health indexes consisting of errors between measured values ​​and predicted values ​​of the first test item of a plurality of other users; causing the processor to execute In the determining step, the user's health index is compared with the health index distribution map to determine the user's pre-disease state. (Appendix 2) The program described in.

[0135] (Appendix 4) the first test item includes a plurality of individual items; The health index distribution chart is a graph having a health index, which is an axis of the health index, which is an error between a measured value and a predicted value of each of the plurality of individual items of the plurality of other users. (Appendix 3) The program described in.

[0136] (Appendix 5) The graph is a logarithmic scale contour density plot. (Appendix 4) The program described in.

[0137] (Appendix 6) The first test item includes at least one of BMI and LH ratio, The second test items include items that are not functionally related to BMI and LH ratio, A program described in any one of (Appendix 1) to (Appendix 5).

[0138] (Appendix 7) When the first test item is BMI, the second test item is excluding weight, height, and waist, (Appendix 6) The program described in.

[0139] (Appendix 8) When the first test item is the LH ratio, the second test item is excluding the HDL cholesterol level, the LDL cholesterol level, and the TOT cholesterol level; (Appendix 6) The program described in.

[0140] (Appendix 9) A method executed by a computer having a processor and a memory, wherein the processor executes all of the steps performed in any of the inventions according to (Appendix 1) to (Appendix 8).

[0141] (Appendix 10) An information processing device comprising a control unit and a memory unit, wherein the control unit executes all of the steps executed in the invention according to any one of (Appendix 1) to (Appendix 8).

[0142] (Appendix 11) A system comprising means for performing all steps performed in any of the inventions according to (Appendix 1) to (Appendix 8). [Explanation of symbols]

[0143] 1. System 10...User terminal 12...Communication IF 13...Input device 14...Output device 15...Memory 16…Storage 19...Processor 20...Server 22...Communication IF 23...Input / output IF 25…Memory 26…Storage 29...Processor

Claims

1. A program to be executed by a computer having a processor and a memory, the program causing the processor to: acquiring measurements of a first test item and a second test item of a user; a step of inputting the acquired second inspection item of the user into a trained model that outputs a predicted value of the first inspection item when a measured value of the second inspection item is input, and outputting the predicted value of the first inspection item of the user; determining a pre-disease state of the user based on the measured value and predicted value of the first test item of the user; presenting the determined non-disease state of the user; A program that executes the following.

2. In the determining step, a health index consisting of an error between a measured value and a predicted value of the first test item of the user is calculated, and the pre-disease state of the user is determined based on the health index of the user. The program according to claim 1.

3. creating a health index distribution map based on health indexes consisting of errors between measured values ​​and predicted values ​​of the first test item of a plurality of other users; causing the processor to execute In the determining step, the user's health index is compared with the health index distribution map to determine the user's pre-disease state. The program according to claim 2.

4. the first test item includes a plurality of individual items; The health index distribution chart is a graph having a health index, which is an axis of the health index, which is an error between a measured value and a predicted value of each of the plurality of individual items of the plurality of other users. The program according to claim 3.

5. The graph is a logarithmic scale contour density plot. The program according to claim 4.

6. the first test item includes at least one of BMI and LH ratio; The second test items include items that are not functionally related to BMI and LH ratio. The program according to claim 1.

7. When the first test item is BMI, the second test item is excluding weight, height, and waist. The program according to claim 6.

8. When the first test item is the LH ratio, the second test item is excluding the HDL cholesterol level, the LDL cholesterol level, and the TOT cholesterol level. The program according to claim 6.

9. A method implemented on a computer having a processor and a memory, wherein the processor performs all of the steps performed in the invention according to any one of claims 1 to 8.

10. 9. An information processing device comprising a control unit and a storage unit, wherein the control unit executes all of the steps executed in the invention according to any one of claims 1 to 8.

11. A system comprising means for executing all steps performed in the invention according to any one of claims 1 to 8.

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